Search Results (4)

Bacillus subtilis NCD-2 demonstrates exceptional biocontrol potential against cotton Verticillium wilt. While previous studies have established its direct antifungal activity (e.g., inhibiting Verticillium dahliae mycelial growth and spore germination), our work reveals a novel mechanism: NCD-2 primes systemic resistance in cotton by activating plant immune-signaling pathways. Firstly, transcriptional profiling uncovered that NCD-2 triggers a defense response in roots analogous to V. dahliae infection, allowing cotton to maintain a more balanced state when confronted with pathogen attacks. Meanwhile, the mutant strains ∆fen and ∆srf—defective in lipopeptide synthesis—also improved cotton resistance to Verticillium wilt by activating partially identical defense pathways in cotton roots. Furthermore, the application of lipopeptide compounds derived from NCD-2, particularly surfactin and fengycin, could enhance host resistance to V. dahliae. Using an RT-qPCR approach, we found that numerous resistance-related genes were induced by these lipopeptide compounds. The up-regulation of SA/JA pathway markers (e.g., NPR1, ICS1, COI1, and LOX1) revealed NCD-2’s activation of plant immune signaling. Using virus-induced gene silencing (VIGS), we conclusively linked SA and JA signaling to NCD-2-induced defense priming. Silencing either pathway abolished resistance, highlighting their indispensable coordination. By bridging mechanistic insights and agricultural applicability, our work positions NCD-2 as a sustainable alternative to conventional fungicides, addressing both crop productivity and environmental health. Full article

The biodiversity of microorganisms is maintained by intricate nets of interactions between competing species. Impaired functionality of human microbiomes correlates with their reduced biodiversity originating from aseptic environmental conditions and antibiotic use. Microbiomes of wild animals are free of these selective pressures. Microbiota provides a protecting shield from invasion by pathogens in the wild, outcompeting their growth in specific ecological niches. We applied ultrahigh-throughput microfluidic technologies for functional profiling of microbiomes of wild animals, including the skin beetle, Siberian lynx, common raccoon dog, and East Siberian brown bear. Single-cell screening of the most efficient killers of the common human pathogen Staphylococcus aureus resulted in repeated isolation of Bacillus pumilus strains. While isolated strains had different phenotypes, all of them displayed a similar set of biosynthetic gene clusters (BGCs) encoding antibiotic amicoumacin, siderophore bacillibactin, and putative analogs of antimicrobials including bacilysin, surfactin, desferrioxamine, and class IId cyclical bacteriocin. Amicoumacin A (Ami) was identified as a major antibacterial metabolite of these strains mediating their antagonistic activity. Genome mining indicates that Ami BGCs with this architecture subdivide into three distinct families, characteristic of the B. pumilus, B. subtilis, and Paenibacillus species. While Ami itself displays mediocre activity against the majority of Gram-negative bacteria, isolated B. pumilus strains efficiently inhibit the growth of both Gram-positive S. aureus and Gram-negative E. coli in coculture. We believe that the expanded antagonistic activity spectrum of Ami-producing B. pumilus can be attributed to the metabolomic profile predetermined by their biosynthetic fingerprint. Ultrahigh-throughput isolation of natural probiotic strains from wild animal microbiomes, as well as their metabolic reprogramming, opens up a new avenue for pathogen control and microbiome remodeling in the food industry, agriculture, and healthcare. Full article

Artificial Neural Networks can be applied for the identification and classification of prospective drug candidates such as complex compounds, including lipopeptide, based on their SMILES string representation. The training of neural networks is done with SMILES strings, which are predictive of structural identification; the ANNs are efficient of correctly classifying all compounds, substructures and their analogues distinguishing the drugs based upon atomic organization to obtain lead optimization in drug discovery. The proficiency of the trained ANN models in recognizing and classifying the analogous compounds was tested for analysis of similar compounds, which were not taken previously for training and achieved results with correct classification in the validation set. The best result was achieved with 10 numbers of hidden layers. The R2 value for training is 0.90586; the R2 value for testing is 0.99508; the R2 value after validation is 0.94151; the final value of R2 for total sets is 0.89456. The graphs are plotted between 21 epochs and mean square error (MSE) to report the performance of the model. The value of 798.1735 for the gradient of the curve after 21 iterations and 6 validation checks was obtained. A successful model was developed for the identification and classification of lipopeptides from their SMILES annotation that efficiently classifies similar compounds and supports in decision making for analogue-based drug discovery. This will help in appropriate lead optimization studies for the prediction of potential anticancer and antimicrobial lipopeptide-based therapeutics. Full article

Most biosurfactants are obtained using costly culture media and purification processes, which limits their wider industrial use. Sustainability of their production processes can be achieved, in part, by using cheap substrates found among agricultural and food wastes or byproducts. In the present study, crude glycerol, a raw material obtained from several industrial processes, was evaluated as a potential low-cost carbon source to reduce the costs of surfactin production by Bacillus subtilis #309. The culture medium containing soap-derived waste glycerol led to the best surfactin production, reaching about 2.8 g/L. To the best of our knowledge, this is the first report describing surfactin production by B. subtilis using stearin and soap wastes as carbon sources. A complete chemical characterization of surfactin analogs produced from the different waste glycerol samples was performed by liquid chromatography–mass spectrometry (LC-MS) and Fourier transform infrared spectroscopy (FTIR). Furthermore, the surfactin produced in the study exhibited good stability in a wide range of pH, salinity and temperatures, suggesting its potential for several applications in biotechnology. Full article